Update markdown

CLAUDE.md

@@ -1,13 +1,23 @@
 # Python Detector Worker - CLAUDE.md
 
 ## Project Overview
-This is a FastAPI-based computer vision detection worker that processes video streams from RTSP/HTTP sources and runs YOLO-based machine learning pipelines for object detection and classification. The system is designed to work within a larger CMS (Content Management System) architecture.
+This is a FastAPI-based computer vision detection worker that processes video streams from RTSP/HTTP sources and runs advanced YOLO-based machine learning pipelines for multi-class object detection and parallel classification. The system features comprehensive database integration, Redis support, and hierarchical pipeline execution designed to work within a larger CMS (Content Management System) architecture.
+
+### Key Features
+- **Multi-Class Detection**: Simultaneous detection of multiple object classes (e.g., Car + Frontal)
+- **Parallel Processing**: Concurrent execution of classification branches using ThreadPoolExecutor
+- **Database Integration**: Automatic PostgreSQL schema management and record updates
+- **Redis Actions**: Image storage with region cropping and pub/sub messaging
+- **Pipeline Synchronization**: Branch coordination with `waitForBranches` functionality
+- **Dynamic Field Mapping**: Template-based field resolution for database operations
 
 ## Architecture & Technology Stack
 - **Framework**: FastAPI with WebSocket support
 - **ML/CV**: PyTorch, Ultralytics YOLO, OpenCV
 - **Containerization**: Docker (Python 3.13-bookworm base)
-- **Data Storage**: Redis integration for action handling
+- **Data Storage**: Redis integration for action handling + PostgreSQL for persistent storage
+- **Database**: Automatic schema management with gas_station_1 database
+- **Parallel Processing**: ThreadPoolExecutor for concurrent classification
 - **Communication**: WebSocket-based real-time protocol
 
 ## Core Components
@@ -24,9 +34,20 @@ This is a FastAPI-based computer vision detection worker that processes video st
 ### Pipeline System (`siwatsystem/pympta.py`)
 - **MPTA file handling** - ZIP archives containing model configurations
 - **Hierarchical pipeline execution** with detection → classification branching
-- **Redis action system** for image saving and message publishing
+- **Multi-class detection** - Simultaneous detection of multiple classes (Car + Frontal)
+- **Parallel processing** - Concurrent classification branches with ThreadPoolExecutor
+- **Redis action system** - Image saving with region cropping and message publishing
+- **PostgreSQL integration** - Automatic table creation and combined updates
 - **Dynamic model loading** with GPU optimization
 - **Configurable trigger classes and confidence thresholds**
+- **Branch synchronization** - waitForBranches coordination for database updates
+
+### Database System (`siwatsystem/database.py`)
+- **DatabaseManager class** for PostgreSQL operations
+- **Automatic table creation** with gas_station_1.car_frontal_info schema
+- **Combined update operations** with field mapping from branch results
+- **Session management** with UUID generation
+- **Error handling** and connection management
 
 ### Testing & Debugging
 - **Protocol test script** (`test_protocol.py`) for WebSocket communication validation
@@ -92,33 +113,61 @@ This is a FastAPI-based computer vision detection worker that processes video st
 
 ## Model Pipeline (MPTA) Format
 
-### Structure
+### Enhanced Structure
 - **ZIP archive** containing models and configuration
-- **pipeline.json** - Main configuration file
+- **pipeline.json** - Main configuration file with Redis + PostgreSQL settings
 - **Model files** - YOLO .pt files for detection/classification
-- **Redis configuration** - Optional for action execution
+- **Multi-model support** - Detection + multiple classification models
 
-### Pipeline Flow
-1. **Detection stage** - YOLO object detection with bounding boxes
-2. **Trigger evaluation** - Check if detected class matches trigger conditions
-3. **Classification stage** - Crop detected region and run classification model
-4. **Action execution** - Redis operations (image saving, message publishing)
+### Advanced Pipeline Flow
+1. **Multi-class detection stage** - YOLO detection of Car + Frontal simultaneously
+2. **Validation stage** - Check for expected classes (flexible matching)
+3. **Database initialization** - Create initial record with session_id
+4. **Redis actions** - Save cropped frontal images with expiration
+5. **Parallel classification** - Concurrent brand and body type classification
+6. **Branch synchronization** - Wait for all classification branches to complete
+7. **Database update** - Combined update with all classification results
 
-### Branch Configuration
+### Enhanced Branch Configuration
 ```json
 {
-  "modelId": "detector-v1",
-  "modelFile": "detector.pt",
-  "triggerClasses": ["car", "truck"],
-  "minConfidence": 0.5,
-  "branches": [{
-    "modelId": "classifier-v1", 
-    "modelFile": "classifier.pt",
-    "crop": true,
-    "triggerClasses": ["car"],
-    "minConfidence": 0.3,
-    "actions": [...]
-  }]
+  "modelId": "car_frontal_detection_v1",
+  "modelFile": "car_frontal_detection_v1.pt",
+  "multiClass": true,
+  "expectedClasses": ["Car", "Frontal"],
+  "triggerClasses": ["Car", "Frontal"],
+  "minConfidence": 0.8,
+  "actions": [
+    {
+      "type": "redis_save_image",
+      "region": "Frontal",
+      "key": "inference:{display_id}:{timestamp}:{session_id}:{filename}",
+      "expire_seconds": 600
+    }
+  ],
+  "branches": [
+    {
+      "modelId": "car_brand_cls_v1",
+      "modelFile": "car_brand_cls_v1.pt",
+      "parallel": true,
+      "crop": true,
+      "cropClass": "Frontal",
+      "triggerClasses": ["Frontal"],
+      "minConfidence": 0.85
+    }
+  ],
+  "parallelActions": [
+    {
+      "type": "postgresql_update_combined",
+      "table": "car_frontal_info",
+      "key_field": "session_id",
+      "waitForBranches": ["car_brand_cls_v1", "car_bodytype_cls_v1"],
+      "fields": {
+        "car_brand": "{car_brand_cls_v1.brand}",
+        "car_body_type": "{car_bodytype_cls_v1.body_type}"
+      }
+    }
+  ]
 }
 ```
 
@@ -173,6 +222,9 @@ docker run -p 8000:8000 -v ./models:/app/models detector-worker
 - **opencv-python**: Computer vision operations
 - **websockets**: WebSocket client/server
 - **redis**: Redis client for action execution
+- **psycopg2-binary**: PostgreSQL database adapter
+- **scipy**: Scientific computing for advanced algorithms
+- **filterpy**: Kalman filtering and state estimation
 
 ## Security Considerations
 - Model files are loaded from trusted sources only
@@ -180,9 +232,46 @@ docker run -p 8000:8000 -v ./models:/app/models detector-worker
 - WebSocket connections handle disconnects gracefully
 - Resource usage is monitored to prevent DoS
 
+## Database Integration
+
+### Schema Management
+The system automatically creates and manages PostgreSQL tables:
+
+```sql
+CREATE TABLE IF NOT EXISTS gas_station_1.car_frontal_info (
+    display_id VARCHAR(255),
+    captured_timestamp VARCHAR(255),
+    session_id VARCHAR(255) PRIMARY KEY,
+    license_character VARCHAR(255) DEFAULT NULL,
+    license_type VARCHAR(255) DEFAULT 'No model available',
+    car_brand VARCHAR(255) DEFAULT NULL,
+    car_model VARCHAR(255) DEFAULT NULL,
+    car_body_type VARCHAR(255) DEFAULT NULL,
+    created_at TIMESTAMP DEFAULT NOW(),
+    updated_at TIMESTAMP DEFAULT NOW()
+);
+```
+
+### Workflow
+1. **Detection**: When both "Car" and "Frontal" are detected, create initial database record with UUID session_id
+2. **Redis Storage**: Save cropped frontal image to Redis with session_id in key
+3. **Parallel Processing**: Run brand and body type classification concurrently
+4. **Synchronization**: Wait for all branches to complete using `waitForBranches`
+5. **Database Update**: Update record with combined classification results using field mapping
+
+### Field Mapping
+Templates like `{car_brand_cls_v1.brand}` are resolved to actual classification results:
+- `car_brand_cls_v1.brand` → "Honda"
+- `car_bodytype_cls_v1.body_type` → "Sedan"
+
 ## Performance Optimizations
 - GPU acceleration when CUDA is available
 - Shared camera streams reduce resource usage
 - Frame queue optimization (single latest frame)
 - Model caching across subscriptions
-- Trigger class filtering for faster inference
+- Trigger class filtering for faster inference
+- Parallel processing with ThreadPoolExecutor for classification branches
+- Multi-class detection reduces inference passes
+- Region-based cropping minimizes processing overhead
+- Database connection pooling and prepared statements
+- Redis image storage with automatic expiration